Email this article 
Open Access
Subscription or Fee Access
Effect of Non-Local Means Filter in a Homomorphic Framework Cascaded with Bacterial Foraging Optimization to Eliminate Speckle
Abstract
Speckle is a kind of multiplicative noise which degrades the image quality and is present in coherent imaging systems. The primary goal of speckle reduction is to removethe speckle without losing much detail contained in an image.To achieve this goal, in this paper, a new technique is proposed for speckle denoising by the use of a Non-Local Means Filter in a homomorphic framework and a bio-inspired optimization technique to reduce speckle while preserving finer details. The statistical results of simulations are done using Matlab and its analysis is presented to demonstrate the advantages and disadvantages of the proposed technique over other standard speckle filters. Thus the speckle denoising technique so developed eliminates the speckle noise and significantly improves the image quality.
Keywords
References
Subhojit Sarker and Swapna Devi, “Speckle denoising using Non-Local Means filter cascaded with Bacterial Foraging Optimization Technique.” Proceedings of the International Conference on Information and Communication Technology ICICT 2010, Madurai, pp. 285-288, December 2010.
Nelson D.A. Mascarenhas, “An overview of speckle noise filtering in SAR images”, Proceedings of the first Latino-American seminar on Radar remote sensing – Image processing techniques, Buenos Aires,Argentina, 2-4 December, 1996.
H. Abrishami Moghaddam, M.J. Valadan Zouj, M. Dehghani,
“Bayesian-based Despeckling In Wavelet Domain Using ‘A Trous’ Algorithm”, Proceedings of the 20th International Congress on Photo grammetry and Remote Sensing ISPRS2004, Istanbul, Turkey, July 2004.
Alin Achim, Anastasios Bezerianos, and Panagiotis Tsakalides, “Novel Bayesian Multiscale Method for Speckle Removal in Medical Ultrasound Images”, IEEE Transactons on medical imaging, Vol. 20, no.8, pp. 772-783, August 2001.
Jain, A.K: ‘Fundamentals of digital image processing’, Prentice-Hall, Englewood Cliffs, NJ, USA, 1989.
A. Buades, B. Coll, and J.M. Morel, “A review of image denoising algorithms, with a new one”. Society for Industrial and Applied Mathematics, Vol. 4, No. 2, pp. 490–530, 2005.
Arash Vosoughi, Mohammad. B. Shamsollahi, “Speckle Noise Reduction of Ultrasound Images Using M-band Wavelet Transform and Wiener Filter in a Homomorphic Framework”, International Conference on Bio-Medical Engineering and Informatics BMEI, Vol. 2, pp. 510-515, 2008.
K.M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control” IEEE Control Systems Magazine, pp. 53–67, June 2002.
Ehsan Nadernejad, Mohammad Reza Karami, Sara Sharifzadeh, Mostafa Heidari, “Despeckle Filtering in Medical Ultrasound Imaging”, Contemporary Engineering Sciences, Vol. 2, no. 1, pp.17 – 36, 2009.
Pierrick Coup´e, Pierre Hellier, Charles Kervrann and Christian Barillot,“Non-Local Means-based Speckle Filtering for Ultrasound Images”INSERM-00428524, Version 2 – 28, October 2009.
Nusrat Ahmed Surobhi and Md. Ruhul Amin, “Employment of Modified Homomorphic Filters In Medical Imaging”, Daffodil International University Journal Of Science And Technology, Vol. 1, Issue 1, July 2006.
Buades, A., Coll, B., Morel, J.-M., ‘A non-local algorithm for image denoising’ Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on Vol. 2, pp. 60 – 65, 20-25 June, 2005.
K.M. Bakwad, S.S. Pattnaik, B.S. Sohi, S. Devi, B.K. Panigrahi, Sastry V.R.S. Gollapudi, “Bacterial Foraging Optimization Technique Cascaded with adaptive filter to enhance peak signal to noise ratio from single image”, IETE Journal of Research, Vol. 55, Issue 4, pp. 173-179,
Jul-Aug 2009.
T. Datta and I.S. Misra, “Improved adaptive bacteria foraging algorithm in optimization of antenna array for faster convergence”, Progress in electromagnetics research, Vol. 1, pp. 143–157, 2008.
Fabrizio Argenti, and Luciano Alparone, “Speckle Removal From SAR Images in the Undecimated Wavelet Domain”, IEEE transactions on geoscience and remote sensing, Vol. 40, No. 11, November 2002.
J.S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2, pp. 165–168, 1980.
D. Kuan, A. Sawchuck, T. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, No.2, pp. 165–177, 1985.
P.C. Tay, S.T. Acton, and J.A. Hossack, “A stochastic approach to ultrasound despeckling,” 3rd IEEE International Symposium in Biomedical Imaging: Nano to Macro, pp. 221–224, 2006.
Y. Yu and S.T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Transactions on Image Processing, Vol. 11, No. 11, pp. 1260–1270,2002.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.